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Latent GOLD 4.5 是一個強大的潛在類別和有限混合程式。Latent
GOLD包含單獨的模組，用於估算三個不同的模型結構：
潛伏類群集模型
離散因數（DFactor）模型
潛伏類回歸模型
功能
完整的 windows 執行-點擊
互動式圖形提供資料和功能強大的模型診斷功能的新見解
靈活的模型結構可以處理不同的標準變數
隨機的起始值集的自動生成
快速、 高效的最大似然和後模式估算基於 EM 和牛頓拉夫演算法
貝葉斯常量，消除邊界解決方案的使用
二元殘餘診斷為本地依賴項 |
Overview
Latent GOLD 4.5 is a powerful latent class and finite mixture
program. Latent GOLD contains separate modules for estimating three
different model structures:
Latent Class Cluster models
Discrete Factor (DFactor) models
Latent Class Regression models
Features
Full windows
implementation - point and click
Interactive
graphics provide new insights into data and powerful model
diagnostic capabilities
Flexible
model structures can handle variables of different metrics
Automatic
generation of sets of random starting values
Fast,
efficient maximum likelihood and posterior mode estimation based on
EM and Newton Raphson algorithms
Use of Bayes
constants to eliminate boundary solutions
Bivariate
residual diagnostic for local dependencies
Capabilities
Known Class Indicator
This feature allows more control over the segment definitions by
pre-assigning selected cases (not) to be in a particular class or
classes.
Conditional Bootstrap p-value
Model difference bootstrap can be used to formally assess the
significance in improvement associated with adding additional
classes, additional DFactors and/or an additional DFactor levels to
the model, or to relax any other model restriction.
Overdispersed (Count and Binomial Count in Regression)
Overdispersion is a common phenomenon in count data. It means
that, as a result of unobserved heterogeneity, the variance of the
count variable is larger than estimated by the Poisson (binomial)
model. The overdispersed option makes it possible to account for
unobserved heterogeneity by assuming that the rates (success
probabilities) follow a gamma (beta) distribution. This yields a
negative-binomial model for overdispersed Poisson counts and a
negative-binomial model for overdispersed binomial counts. Note that
this option is conceptually similar to including a normally
distributed random intercept in a regression model for a count
variable.
The overdispersion option is useful if one wishes to analyze count
data using mixture or zero-inflated variants of (truncated)
negative-binomial or beta-binomial models (Agresti, 2000; Long,
1997; Simonoff, 2003). The negative-binomial model is a Poisson
model with an extra error term coming from a gamma distribution. The
beta-binomial model is a variant of the binomial count model that
assumes that the success probabilities come from a beta
distribution. These models are common in fields such as criminology,
political sciences, medicine, biology, and marketing.
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